code: def pareto(
      random_state, a, size=None, chunk_size=None, gpu=None, dtype=None
  ):
      r"""
      Draw samples from a Pareto II or Lomax distribution with
      specified shape.
  
      The Lomax or Pareto II distribution is a shifted Pareto
      distribution. The classical Pareto distribution can be
      obtained from the Lomax distribution by adding 1 and
      multiplying by the scale parameter ``m`` (see Notes).  The
      smallest value of the Lomax distribution is zero while for the
      classical Pareto distribution it is ``mu``, where the standard
      Pareto distribution has location ``mu = 1``.  Lomax can also
      be considered as a simplified version of the Generalized
      Pareto distribution (available in SciPy), with the scale set
      to one and the location set to zero.
  
      The Pareto distribution must be greater than zero, and is
      unbounded above.  It is also known as the "80-20 rule".  In
      this distribution, 80 percent of the weights are in the lowest
      20 percent of the range, while the other 20 percent fill the
      remaining 80 percent of the range.
  
      Parameters
      ----------
      a : float or array_like of floats
          Shape of the distribution. Should be greater than zero.
      size : int or tuple of ints, optional
          Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
          ``m * n * k`` samples are drawn.  If size is ``None`` (default),
          a single value is returned if ``a`` is a scalar.  Otherwise,
          ``mt.array(a).size`` samples are drawn.
      chunk_size : int or tuple of int or tuple of ints, optional
          Desired chunk size on each dimension
      gpu : bool, optional
          Allocate the tensor on GPU if True, False as default
      dtype : data-type, optional
        Data-type of the returned tensor.
  
      Returns
      -------
      out : Tensor or scalar
          Drawn samples from the parameterized Pareto distribution.
  
      See Also
      --------
      scipy.stats.lomax : probability density function, distribution or
          cumulative density function, etc.
      scipy.stats.genpareto : probability density function, distribution or
          cumulative density function, etc.
  
      Notes
      -----
      The probability density for the Pareto distribution is
  
      .. math:: p(x) = \frac{am^a}{x^{a+1}}
  
      where :math:`a` is the shape and :math:`m` the scale.
  
      The Pareto distribution, named after the Italian economist
      Vilfredo Pareto, is a power law probability distribution
      useful in many real world problems.  Outside the field of
      economics it is generally referred to as the Bradford
      distribution. Pareto developed the distribution to describe
      the distribution of wealth in an economy.  It has also found
      use in insurance, web page access statistics, oil field sizes,
      and many other problems, including the download frequency for
      projects in Sourceforge [1]_.  It is one of the so-called
      "fat-tailed" distributions.
  
  
      References
      ----------
      .. [1] Francis Hunt and Paul Johnson, On the Pareto Distribution of
             Sourceforge projects.
      .. [2] Pareto, V. (1896). Course of Political Economy. Lausanne.
      .. [3] Reiss, R.D., Thomas, M.(2001), Statistical Analysis of Extreme
             Values, Birkhauser Verlag, Basel, pp 23-30.
      .. [4] Wikipedia, "Pareto distribution",
             http://en.wikipedia.org/wiki/Pareto_distribution
  
      Examples
      --------
      Draw samples from the distribution:
  
      >>> import mars.tensor as mt
  
      >>> a, m = 3., 2.  # shape and mode
      >>> s = (mt.random.pareto(a, 1000) + 1) * m
  
      Display the histogram of the samples, along with the probability
      density function:
  
      >>> import matplotlib.pyplot as plt
      >>> count, bins, _ = plt.hist(s.execute(), 100, normed=True)
      >>> fit = a*m**a / bins**(a+1)
      >>> plt.plot(bins, max(count)*fit/max(fit), linewidth=2, color='r')
      >>> plt.show()
      """
      if dtype is None:
          dtype = (
              np.random.RandomState()
              .pareto(handle_array(a), size=(0,))
              .dtype
          )
      size = random_state._handle_size(size)
      seed = gen_random_seeds(1, random_state.to_numpy())[0]
      op = TensorPareto(size=size, seed=seed, gpu=gpu, dtype=dtype)
      return op(a, chunk_size=chunk_size)
  
file_path: mars/mars/tensor/random/pareto.py
imports: import numpy
  from ...None import opcodes
  from ...serialization.serializables import AnyField
  from ..utils import gen_random_seeds
  from .core import TensorRandomOperandMixin, handle_array, TensorDistribution
label_desc: Use image node_id 1 for calling a global function with example usage: pareto(random_state, a, size, chunk_size, gpu, dtype) and returns: op